Automatic crack detection on road pavements using encoder-decoder architecture

Zhun Fan, Chong Li, Ying Chen, Jiahong Wei, Giuseppe Loprencipe*, Xiaopeng Chen, Paola Di Mascio

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

103 Citations (Scopus)

Abstract

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low-level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.

Original languageEnglish
Article number2960
Pages (from-to)1-18
Number of pages18
JournalMaterials
Volume13
Issue number13
DOIs
Publication statusPublished - 1 Jul 2020

Keywords

  • Automatic crack detection
  • Deep learning
  • Dilated convolution
  • Encoder-decoder
  • Hierarchical feature
  • Pavement cracking
  • U-net

Fingerprint

Dive into the research topics of 'Automatic crack detection on road pavements using encoder-decoder architecture'. Together they form a unique fingerprint.

Cite this